Abstract: Digital signal processing and machine learning techniques can be employed in a vocal capture and performance social network to computationally generate vocal pitch tracks from a collection of vocal performances captured against a common temporal baseline such as a backing track or an original performance by a popularizing artist. In this way, crowd-sourced pitch tracks may be generated and distributed for use in subsequent karaoke-style vocal audio captures or other applications. Large numbers of performances of a song can be used to generate a pitch track. Computationally determined pitch trackings from individual audio signal encodings of the crowd-sourced vocal performance set are aggregated and processed as an observation sequence of a trained Hidden Markov Model (HMM) or other statistical model to produce an output pitch track.

Abstract: Vocal audio of a user together with performance synchronized video is captured and coordinated with audiovisual contributions of other users to form composite duet-style or glee club-style or window-paned music video-style audiovisual performances. In some cases, the vocal performances of individual users are captured (together with performance synchronized video) on mobile devices, television-type display and/or set-top box equipment in the context of karaoke-style presentations of lyrics in correspondence with audible renderings of a backing track. Contributions of multiple vocalists are coordinated and mixed in a manner that selects for presentation, at any given time along a given performance timeline, performance synchronized video of one or more of the contributors. Selections are in accord with a visual progression that codes a sequence of visual layouts in correspondence with other coded aspects of a performance score such as pitch tracks, backing audio, lyrics, sections and/or vocal parts.

Abstract: Digital signal processing and machine learning techniques can be employed in a vocal capture and performance social network to computationally generate vocal pitch tracks from a collection of vocal performances captured against a common temporal baseline such as a backing track or an original performance by a popularizing artist. In this way, crowd-sourced pitch tracks may be generated and distributed for use in subsequent karaoke-style vocal audio captures or other applications. Large numbers of performances of a song can be used to generate a pitch track. Computationally determined pitch trackings from individual audio signal encodings of the crowd-sourced vocal performance set are aggregated and processed as an observation sequence of a trained Hidden Markov Model (HMM) or other statistical model to produce an output pitch track.

Abstract: Vocal audio of a user together with performance synchronized video is captured and coordinated with audiovisual contributions of other users to form composite duet-style or glee club-style or window-paned music video-style audiovisual performances. In some cases, the vocal performances of individual users are captured (together with performance synchronized video) on mobile devices, television-type display and/or set-top box equipment in the context of karaoke-style presentations of lyrics in correspondence with audible renderings of a backing track. Contributions of multiple vocalists are coordinated and mixed in a manner that selects for presentation, at any given time along a given performance timeline, performance synchronized video of one or more of the contributors. Selections are in accord with a visual progression that codes a sequence of visual layouts in correspondence with other coded aspects of a performance score such as pitch tracks, backing audio, lyrics, sections and/or vocal parts.

Abstract: Embodiments described herein relate generally to systems comprising a display device, a display device-coupled computing platform, a mobile device in communication with the computing platform, and a content server in which methods and techniques of capture and/or processing of audiovisual performances are described and, in particular, description of techniques suitable for use in connection with display device connected computing platforms for rendering vocal performance captured by a handheld computing device.

Abstract: Latency on different devices (e.g., devices of differing brand, model, vintage, etc.) can vary significantly and tens of milliseconds can affect human perception of lagging and leading components of a performance. As a result, use of a uniform latency estimate across a wide variety of devices is unlikely to provide good results, and hand-estimating round-trip latency across a wide variety of devices is costly and would constantly need to be updated for new devices. Instead, a system has been developed for crowdsourcing latency estimates.

Abstract: Digital signal processing and machine learning techniques can be employed in a vocal capture and performance social network to computationally generate vocal pitch tracks from a collection of vocal performances captured against a common temporal baseline such as a backing track or an original performance by a popularizing artist. In this way, crowd-sourced pitch tracks may be generated and distributed for use in subsequent karaoke-style vocal audio captures or other applications. Large numbers of performances of a song can be used to generate a pitch track. Computationally determined pitch trackings from individual audio signal encodings of the crowd-sourced vocal performance set are aggregated and processed as an observation sequence of a trained Hidden Markov Model (HMM) or other statistical model to produce an output pitch track.

Abstract: Vocal audio of a user together with performance synchronized video is captured and coordinated with audiovisual contributions of other users to form composite duet-style or glee club-style or window-paned music video-style audiovisual performances. In some cases, the vocal performances of individual users are captured (together with performance synchronized video) on mobile devices, television-type display and/or set-top box equipment in the context of karaoke-style presentations of lyrics in correspondence with audible renderings of a backing track. Contributions of multiple vocalists are coordinated and mixed in a manner that selects for presentation, at any given time along a given performance timeline, performance synchronized video of one or more of the contributors. Selections are in accord with a visual progression that codes a sequence of visual layouts in correspondence with other coded aspects of a performance score such as pitch tracks, backing audio, lyrics, sections and/or vocal parts.

Abstract: Latency on different devices (e.g., devices of differing brand, model, vintage, etc.) can vary significantly and tens of milliseconds can affect human perception of lagging and leading components of a performance. As a result, use of a uniform latency estimate across a wide variety of devices is unlikely to provide good results, and hand-estimating round-trip latency across a wide variety of devices is costly and would constantly need to be updated for new devices. Instead, a system has been developed for crowdsourcing latency estimates.

Abstract: Embodiments described herein relate generally to systems comprising a display device, a display device-coupled computing platform, a mobile device in communication with the computing platform, and a content server in which methods and techniques of capture and/or processing of audiovisual performances are described and, in particular, description of techniques suitable for use in connection with display device connected computing platforms for rendering vocal performance captured by a handheld computing device.

Abstract: Digital signal processing and machine learning techniques can be employed in a vocal capture and performance social network to computationally generate vocal pitch tracks from a collection of vocal performances captured against a common temporal baseline such as a backing track or an original performance by a popularizing artist. In this way, crowd-sourced pitch tracks may be generated and distributed for use in subsequent karaoke-style vocal audio captures or other applications. Large numbers of performances of a song can be used to generate a pitch track. Computationally determined pitch trackings from individual audio signal encodings of the crowd-sourced vocal performance set are aggregated and processed as an observation sequence of a trained Hidden Markov Model (HMM) or other statistical model to produce an output pitch track.

Abstract: Vocal audio of a user together with performance synchronized video is captured and coordinated with audiovisual contributions of other users to form composite duet-style or glee club-style or window-paned music video-style audiovisual performances. In some cases, the vocal performances of individual users are captured (together with performance synchronized video) on mobile devices, television-type display and/or set-top box equipment in the context of karaoke-style presentations of lyrics in correspondence with audible renderings of a backing track. Contributions of multiple vocalists are coordinated and mixed in a manner that selects for presentation, at any given time along a given performance timeline, performance synchronized video of one or more of the contributors. Selections are in accord with a visual progression that codes a sequence of visual layouts in correspondence with other coded aspects of a performance score such as pitch tracks, backing audio, lyrics, sections and/or vocal parts.

Abstract: Embodiments described herein relate generally to systems comprising a display device, a display device-coupled computing platform, a mobile device in communication with the computing platform, and a content server in which methods and techniques of capture and/or processing of audiovisual performances are described and, in particular, description of techniques suitable for use in connection with display device connected computing platforms for rendering vocal performance captured by a handheld computing device.

Abstract: Latency on different devices (e.g., devices of differing brand, model, vintage, etc.) can vary significantly and tens of milliseconds can affect human perception of lagging and leading components of a performance. As a result, use of a uniform latency estimate across a wide variety of devices is unlikely to provide good results, and hand-estimating round-trip latency across a wide variety of devices is costly and would constantly need to be updated for new devices. Instead, a system has been developed for crowdsourcing latency estimates.

Abstract: Digital signal processing and machine learning techniques can be employed in a vocal capture and performance social network to computationally generate vocal pitch tracks from a collection of vocal performances captured against a common temporal baseline such as a backing track or an original performance by a popularizing artist. In this way, crowd-sourced pitch tracks may be generated and distributed for use in subsequent karaoke-style vocal audio captures or other applications. Large numbers of performances of a song can be used to generate a pitch track. Computationally determined pitch trackings from individual audio signal encodings of the crowd-sourced vocal performance set are aggregated and processed as an observation sequence of a trained Hidden Markov Model (HMM) or other statistical model to produce an output pitch track.

Abstract: Vocal audio of a user together with performance synchronized video is captured and coordinated with audiovisual contributions of other users to form composite duet-style or glee club-style or window-paned music video-style audiovisual performances. In some cases, the vocal performances of individual users are captured (together with performance synchronized video) on mobile devices, television-type display and/or set-top box equipment in the context of karaoke-style presentations of lyrics in correspondence with audible renderings of a backing track. Contributions of multiple vocalists are coordinated and mixed in a manner that selects for presentation, at any given time along a given performance timeline, performance synchronized video of one or more of the contributors. Selections are in accord with a visual progression that codes a sequence of visual layouts in correspondence with other coded aspects of a performance score such as pitch tracks, backing audio, lyrics, sections and/or vocal parts.

Abstract: Latency on different devices (e.g., devices of differing brand, model, vintage, etc.) can vary significantly and tens of milliseconds can affect human perception of lagging and leading components of a performance. As a result, use of a uniform latency estimate across a wide variety of devices is unlikely to provide good results, and hand-estimating round-trip latency across a wide variety of devices is costly and would constantly need to be updated for new devices. Instead, a system has been developed for crowdsourcing latency estimates.

Abstract: Vocal audio of a user together with performance synchronized video is captured and coordinated with audiovisual contributions of other users to form composite duet-style or glee club-style or window-paned music video-style audiovisual performances. In some cases, the vocal performances of individual users are captured (together with performance synchronized video) on mobile devices, television-type display and/or set-top box equipment in the context of karaoke-style presentations of lyrics in correspondence with audible renderings of a backing track. Contributions of multiple vocalists are coordinated and mixed in a manner that selects for presentation, at any given time along a given performance timeline, performance synchronized video of one or more of the contributors. Selections are in accord with a visual progression that codes a sequence of visual layouts in correspondence with other coded aspects of a performance score such as pitch tracks, backing audio, lyrics, sections and/or vocal parts.